CN105610841B - User information authentication method based on traceability - Google Patents
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Abstract
The invention provides a user information authentication method based on traceability, which comprises the following steps: authenticating the identity of the user; constructing a behavior authentication set; performing credible authentication on user behaviors based on a D-S evidence theory; tracing and authenticating the user identity. The technical scheme solves the problem of identity authentication of network users in the Internet, and the method can realize dual authentication of the identity and the behavior of the user, thereby not only effectively avoiding illegal users from entering the network, but also standardizing the network behavior of the users; thereby greatly improving the security of the internet.
Description
Technical Field
The invention relates to an authentication method, in particular to a user information authentication method based on traceability.
Background
With the continuous popularization and deepening of the application of network information systems, the information security problem is more and more emphasized, and in order to guarantee the security of the information systems, the terminal security protection becomes a difficult problem which needs to be solved urgently.
The terminal user authentication technology is used as a first gateway for the security of computers and network systems, and is an important technical means for confirming the identity of an operator. At present, the commonly adopted safety protection measures are used for issuing digital certificates, USB user keys, biological characteristics (fingerprints and irises) and other auxiliary safety measures to the users to verify the authenticity of the user identities and guarantee the safety of the users in the internet activities.
In recent years, mobile terminals are developed more and more rapidly, and due to the problems of limitation of hardware facilities of the mobile terminals, various types of mobile phone operating systems and the like, the traditional safety protection measures are not suitable; in addition, the traditional identity authentication method confirms the identity of the user, but the identity trust is not enough, because the identity of the user is trusted in some cases, but the behavior of the user is not necessarily trusted. Therefore, the dual trustworthiness of the user identity and behavior must be guaranteed.
At present, the technologies for identity authentication of internet users mainly include a password mechanism and a biometric identification technology, but both methods have respective limitations. In particular, the drawbacks of the password mechanism are: the password is transmitted to the verification server through the plaintext, the password is easy to intercept and capture, and the password maintenance cost is high; however, in order to ensure the security, the password should be changed frequently, and in addition, in order to avoid dictionary attack on the password, the password should be ensured to be of a certain length and is difficult to memorize; the password is easy to be peeped by others when being input. The disadvantages of the biometric identification technology are: accurate experimental equipment required for biometric identification is often expensive and can change due to many biological characteristics of a person; therefore, the method has low stability and high recognition failure rate.
Disclosure of Invention
In order to overcome the defects, the invention provides a user information authentication method based on traceability, which avoids the misjudgment of user identity authentication and solves the problem of detecting illegal users in network activities; the safety of the internet is improved.
The purpose of the invention is realized by adopting the following technical scheme:
a traceability-based user information authentication method, the method comprising the steps of:
1) authenticating the identity of the user;
2) constructing a behavior authentication set;
3) performing credible authentication on user behaviors based on a D-S evidence theory;
4) tracing and authenticating the user identity.
Preferably, in the step 1), authenticating the user identity includes that the user sends a request message to the server, and the server extracts the user IP address and the negation operation rule of the binary number contained in the message after receiving the request message;
the negation operation rule is utilized to negate the IP address of the user according to the bit, a dynamic password is generated, and the dynamic password is returned to the user;
after obtaining the dynamic password, the user fills in the user name and the dynamic password and submits the user name and the dynamic password to the server for authentication, if the authentication is successful, the user is allowed to enter the network system, and the step 2) is carried out; otherwise, the login fails.
Preferably, the step 2) of constructing the behavior authentication set data includes content abnormal data, habit abnormal data, security abnormal data and contract abnormal data, and the step 3) of performing trusted authentication on the user behavior based on the D-S evidence theory includes:
3-1 capturing user behavior according to message content submitted by a user in network flow; according to the behavior authentication set, dividing the behavior of the user into credible behavior and incredible behavior;
3-2, determining a user behavior credibility evaluation function;
3-3, performing credible authentication on the user behavior.
Further, the dividing method of step 3-1 includes: setting the behavior authentication set to S ═ S1，S2，S3......SnThe quality of the k-th action is Sk(ii) a Wherein k is more than or equal to 1 and less than or equal to n;
defining credibility threshold values alpha and beta of a behavior authentication set, and satisfying that alpha is more than or equal to 0 and less than or equal to beta is less than or equal to 1; when beta is less than or equal to SkWhen the number is less than or equal to 1, the kth interaction line is credible; when 0 is less than or equal to SkWhen alpha is less than or equal to alpha, the interaction behavior is not credible; if alpha is less than or equal to SkAnd if the k time of the interactive behavior is less than or equal to beta, the k time of the interactive behavior is uncertain behavior.
Further, the user behavior reliability evaluation function in step 3-2 includes a probability distribution function of a trusted behavior, a probability distribution function of an untrusted behavior, and a probability distribution function of an uncertain behavior, and specifically includes:
setting a sample space D ═ { T, -T }, and setting a probability distribution function of credible behaviors as m { T } ═ p × length [ ]TIs represented by/N, wherein 0<p<1, { T } is a sequence of trusted activities, lengthTThe length of the credible behavior sequence is shown, and p is a credible behavior adjustment coefficient;
the probability distribution function of the untrusted behavior is given by the equation m { -T } ═ q × length-TIs represented by/N, wherein 0<q<1, { -T } is a sequence of untrusted behaviors, length-TIs the length of the sequence of the unreliable behaviors, and q is the adjustment coefficient of the unreliable behaviors.
Further, defining the user behaviors which do not belong to the credible behavior sequence { T } and the incredible behavior sequence { -T } as an uncertain behavior sequence { T, -T }, and generating a user behavior credibility evaluation function triple < m { T }, m { -T }, m { T, -T } >; where m { T, -T } represents a probability distribution function for the uncertain behavior.
Further, in step 3-3, the method for authenticating the trust of the user behavior includes: normalizing the user behavior credibility evaluation function to obtain the credibility behavior occurrence probability; if the probability range does not exceed the preset threshold, the user behavior is credible, the operation is allowed to continue, otherwise, the step 4) is skipped; wherein the content of the first and second substances,
the user behavior credibility evaluation function after the normalization processing is shown as the following formula:
wherein T is a credible behavior set, and P (T) is the occurrence probability of credible behaviors; then the process of the first step is carried out,
P(T)＝(1-β)/(1-β+ɑ).Bel(T)＝m(T)＝p*lengthT/N,pl(T)＝1-Bel(-T)
wherein Pl (T) is a likelihood function of the set of trusted behaviors; bel (T) is a trust function for a set of trusted behaviors.
Preferably, the source tracing and the user identity confirmation in the step 4) comprise: if the user behavior is not credible, the system service provider automatically provides an identity tracing request to the server, performs secondary authentication on the user, and judges whether false information exists in the user identity and the behavior.
Further, the secondary authentication includes: after receiving an identity traceability request of a system service provider, the server returns a traceability information code of a user corresponding to the credible behavior occurrence probability range exceeding a preset threshold to the system service provider; comparing whether the returned tracing information code is the same as the original tracing information code preset in the SP server or not; the source tracing information code comprises user registration information and historical behaviors.
Compared with the prior art, the invention has the following beneficial effects:
in the scheme of the invention, in the aspect of password generation, the IP address of the mobile terminal held by the legal user is used for negation to generate the dynamic password, so that not only can the illegal user be effectively prevented from logging in on other mobile equipment by using the current user account, but also the people are prevented from using too simple passwords such as birthday numbers and the like, and the difficulty of password cracking is increased; the defect that the password of the user is easy to crack is effectively avoided, and the safety is greatly improved.
The invention provides a behavior authentication set based on user behavior authentication, which makes up the defect that the traditional authentication process neglects to detect the user behavior and standardizes the network behavior of the user;
the invention not only authenticates the user identity, but also authenticates the user behavior, thereby effectively avoiding illegal users from entering a network system and standardizing the network behavior of the users; after the action authentication fails, the identity tracing confirmation is carried out again, and the condition of identity misjudgment can be effectively avoided through secondary confirmation, so that the authentication accuracy is improved.
Drawings
FIG. 1 is a flowchart of a traceability-based user information authentication method;
FIG. 2 is a timing diagram of a traceability-based user information authentication scheme;
Detailed Description
The following describes embodiments of the present invention in further detail with reference to the accompanying drawings.
As shown in fig. 1, a traceability-based user information authentication method includes the following steps:
1) authenticating the identity of the user; in the step 1), the user identity authentication comprises that a user sends a request message to a server, and the server extracts the user IP address and the negation operation rule of binary number contained in the message after receiving the message;
the negation operation rule is utilized to negate the IP address of the user according to the bit, a dynamic password is generated, and the dynamic password is returned to the user;
after obtaining the dynamic password, the user fills in the user name and the dynamic password and submits the user name and the dynamic password to the server for authentication, if the authentication is successful, the user is allowed to enter the network system, and the step 2) is carried out; otherwise, the login fails.
2) Constructing a behavior authentication set; the behavior authentication set in the step 2) is constructed according to network security rules; which includes content exception data, habit exception data, security exception data, and contract exception data.
Acquiring content abnormal data: for example, students in a certain specialty download learning materials irrelevant to their specialty in large quantities; a user suddenly purchases a large amount of past unconventional goods.
Acquiring habit abnormal data: each terminal user has a unique behavior operation habit when using cloud computing resources, for example, for an old user who is familiar with the cloud resources, an operation flow may be that the user directly clicks the resources which are commonly used in the past, uses the resources, releases the resources, and the like. For a fraudster, the habits of using the network may be different from the original user;
acquiring safety abnormal data: the abnormal behavior can form huge potential safety hazard to the system to cause system damage, and the abnormal behavior can be used as an authentication set for behavior safety detection according to the current intrusion detection rule;
acquiring contract abnormal data: for important network services, cloud service providers sign service contracts with end users, and illegal users or some legal users who seek to be out of track violate contract regulations in user behaviors.
3) Performing credible authentication on user behaviors based on a D-S evidence theory;
the theory-related knowledge of D-S evidence is introduced as follows:
definition 1: let D be the sample space, propositions within the domain are all represented by a subset of D
Definition 2: let function M: 2D- > [0,1], and satisfies M (Φ) 0, Σ M (a) 1, where a is a subset of D. Then M is the probability distribution function on 2D, and M (A) is the base probability number for A.
Definition 3: propositional trust function Bel: 2D- > [0,1], and bel (a) ═ Σ m (B) (where B is all subsets of a).
Definition 4: likelihood function Pl: 2D- > [0,1], and pl (a) ═ 1-Bel (-a).
The method comprises the following specific steps:
3-1 capturing user behavior according to message content submitted by a user in network flow; according to the behavior authentication set, dividing the behavior of the user into credible behavior and incredible behavior;
3-2, determining a user behavior credibility evaluation function;
3-3, performing credible authentication on the user behavior.
The dividing method of the step 3-1 comprises the following steps: setting the behavior authentication set to S ═ S1，S2，S3......Sn}; the k-th action quality is Sk(ii) a Wherein k is more than or equal to 1 and less than or equal to n;
defining credibility threshold values alpha and beta of a behavior authentication set, and satisfying that alpha is more than or equal to 0 and less than or equal to beta is less than or equal to 1; when beta is less than or equal to SkWhen the number is less than or equal to 1, the kth interaction line is credible; when 0 is less than or equal to SkWhen alpha is less than or equal to alpha, the interaction behavior is not credible; if alpha is less than or equal to SkAnd if the k time of the interactive behavior is less than or equal to beta, the k time of the interactive behavior is uncertain behavior.
The user behavior credibility evaluation function in the step 3-2 comprises a probability distribution function of a credible behavior, a probability distribution function of an incredible behavior and a probability distribution function of an uncertain behavior, and specifically comprises the following steps:
setting a sample space D ═ { T, -T }, and setting a probability distribution function of credible behaviors as m { T } ═ p × length [ ]TIs represented by/N, wherein 0<p<1, { T } is a sequence of trusted activities, lengthTThe length of the credible behavior sequence is shown, and p is a credible behavior adjustment coefficient;
the probability distribution function of the untrusted behavior is given by the equation m { -T } ═ q × length-TIs represented by/N, wherein 0<q<1, { -T } is a sequence of untrusted behaviors, length-TThe length of the sequence of the unreliable behaviors and q is an unreliable behavior adjusting coefficient; the value range of q is adjusted according to the tolerance of the network to the untrusted behavior of the user, and the higher the tolerance is, the larger the q value is;
defining user behaviors which do not belong to a credible behavior sequence { T } and an incredible behavior sequence { -T } as an uncertain behavior sequence { T, -T }, and generating a user behavior credibility evaluation function triple < m { T }, m { -T }, m { T, -T } >; where m { T, -T } represents a probability distribution function for the uncertain behavior.
The credible authentication method of the user behavior in the step 3-3 comprises the following steps: normalizing the user behavior credibility evaluation function to obtain the credibility behavior occurrence probability; if the probability range does not exceed the preset threshold, the user behavior is credible, the operation is allowed to continue, otherwise, the step 4) is skipped; wherein the content of the first and second substances,
the user behavior credibility evaluation function after the normalization processing is shown as the following formula:
wherein T is a credible behavior set, and P (T) is the occurrence probability of credible behaviors; then the process of the first step is carried out,
P(T)＝(1-β)/(1-β+ɑ).Bel(T)＝m(T)＝p*lengthT/N,pl(T)＝1-Bel(-T)
wherein Pl (T) is a likelihood function of the set of trusted behaviors; bel (T) is a trust function for a set of trusted behaviors.
4) Tracing and authenticating the user identity. If the user behavior is not credible, the system service provider automatically provides an identity tracing request to the server, performs secondary authentication on the user, and judges whether false information exists in the user identity and the behavior; therefore, identity misjudgment is avoided, and user network behaviors are normalized.
The secondary authentication comprises: after receiving an identity traceability request of a system service provider, the server returns a traceability information code of a user corresponding to the credible behavior occurrence probability range exceeding a preset threshold to the system service provider; comparing whether the returned tracing information code is the same as the original tracing information code preset in the SP server or not; the source tracing information code comprises user registration information and historical behaviors.
Finally, it should be noted that: the above embodiments are only used for illustrating the technical solutions of the present application and not for limiting the protection scope thereof, and although the present application is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: numerous variations, modifications, and equivalents will occur to those skilled in the art upon reading the present application and are within the scope of the claims appended hereto.
Claims (7)
1. A user information authentication method based on traceability is characterized by comprising the following steps:
1) authenticating the identity of the user;
2) constructing a behavior authentication set;
3) performing credible authentication on user behaviors based on a D-S evidence theory;
4) tracing and authenticating the identity of the user;
the constructed behavior authentication set data in the step 2) comprises content abnormal data, habit abnormal data, safety abnormal data and contract abnormal data;
the step 3) of performing credible authentication on the user behavior based on the D-S evidence theory comprises the following steps:
3-1 capturing user behavior according to message content submitted by a user in network flow; according to the behavior authentication set, dividing the behavior of the user into credible behavior and incredible behavior;
3-2, determining a user behavior credibility evaluation function;
3-3, carrying out credible authentication on the user behavior;
the dividing method of the step 3-1 comprises the following steps: setting the behavior authentication set to S ═ S1，S2，S3......Sn}; the k-th action quality is Sk(ii) a Wherein k is more than or equal to 1 and less than or equal to n;
defining credibility threshold values alpha and beta of a behavior authentication set, and satisfying that alpha is more than or equal to 0 and less than or equal to beta is less than or equal to 1; when beta is less than or equal to SkWhen the number is less than or equal to 1, the kth interaction line is credible; when 0 is less than or equal to SkWhen alpha is less than or equal to alpha, the interaction behavior is not credible; if alpha is less than or equal to SkAnd if the k time of the interactive behavior is less than or equal to beta, the k time of the interactive behavior is uncertain behavior.
2. The method according to claim 1, wherein in step 1), authenticating the user identity comprises that the user sends a request message to the server, and the server extracts the inversion operation rule of the user IP address and the binary number contained in the message after receiving the request message;
the negation operation rule is utilized to negate the IP address of the user according to the bit, a dynamic password is generated, and the dynamic password is returned to the user;
after obtaining the dynamic password, the user fills in the user name and the dynamic password and submits the user name and the dynamic password to the server for authentication, if the authentication is successful, the user is allowed to enter the network system, and the step 2) is carried out; otherwise, the login fails.
3. The method according to claim 1, wherein the user behavior credibility assessment function of step 3-2 comprises a probability distribution function of credible behaviors, a probability distribution function of incredible behaviors, and a probability distribution function of uncertain behaviors, specifically:
setting a sample space D ═ { T, -T }, and setting a probability distribution function of credible behaviors as m { T } ═ p × length [ ]TIs represented by/N, wherein 0<p<1, { T } is a sequence of trusted activities, lengthTThe length of the credible behavior sequence is shown, and p is a credible behavior adjustment coefficient; n represents the number of trusted behaviors;
the probability distribution function of the untrusted behavior is given by the equation m { -T } ═ q × length-TIs represented by/N, wherein 0<q<1, { -T } is a sequence of untrusted behaviors, length-TThe length of the sequence of the unreliable behaviors, q is an unreliable behavior adjustment coefficient, and N represents the number of times of the unreliable behaviors.
4. The method of claim 3, wherein user behaviors that do not belong to both the sequence of trustworthy behaviors { T } and the sequence of untrustworthy behaviors { -T } are defined as a sequence of uncertain behaviors { T, -T }, generating a user behavior trustworthiness evaluation function triplet < m { T }, m { -T }, m { T, -T } >; where m { T, -T } represents a probability distribution function for the uncertain behavior.
5. The method of claim 1, wherein the step 3-3 of the trusted authentication method of user behavior comprises: normalizing the user behavior credibility evaluation function to obtain the credibility behavior occurrence probability; if the probability range does not exceed the preset threshold, the user behavior is credible, the operation is allowed to continue, otherwise, the step 4) is skipped; wherein the content of the first and second substances,
the user behavior credibility evaluation function after the normalization processing is shown as the following formula:
wherein T is a credible behavior set, and P (T) is the occurrence probability of credible behaviors; then the process of the first step is carried out,
P(T)＝(1-β)/(1-β+ɑ)·Bel(T)＝m(T)＝p*lengthT/N,pl(T)＝1-Bel(-T)
wherein Pl (T) is a likelihood function of the set of trusted behaviors; bel (T) is a trust function of a set of trusted behaviors; n represents the number of trusted activities.
6. The method of claim 1, wherein the step 4) of tracing the source to confirm the user identity comprises: if the user behavior is not credible, the system service provider automatically provides an identity tracing request to the server, performs secondary authentication on the user, and judges whether false information exists in the user identity and the behavior.
7. The method of claim 6, wherein the secondary authentication comprises: after receiving an identity traceability request of a system service provider, the server returns a traceability information code of a user corresponding to the credible behavior occurrence probability range exceeding a preset threshold to the system service provider; comparing whether the returned tracing information code is the same as the original tracing information code preset in the SP server or not; the source tracing information code comprises user registration information and historical behaviors.
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